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to analyse a bigger data set and build a multi-parameter machine learning (ML) model that integrate the complex mechanical properties obtained from DT and the electric/magnetic permeability data obtained from
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and engineering in the context of Singapore’s coast to build strong features that can be used to predict occurrences of storm surges Implement and test different machine-learning models to evaluate
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the project by: analyzing data on the key protein properties generated by our experimental team, using data analytics or machine learning techniques, linking protein sequences, mass spectrometry data and
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technology. Key Responsibilities: Collaborate with partners from both the academia and the industry to lead and/or conduct innovative research on, but not limited to transfer learning, explainable machine
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techniques, sensor and operation data fusion, data analytics and machine learning algorithms. The primary responsibility of this role is to deliver the program objectives for remote pilotage. Key
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behavior analysis. Proficient in programming in C and Python, with experience in embedded Linux environments. Strong analytical skills, with proficiency in using AI and machine learning technologies
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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machine learning algorithms for electrical vessels condition monitoring, fault diagnosis and early fault prediction. The primary responsibility of this role is to deliver on Ship-level intelligent Equipment
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is mandatory. • Experience in the development of novel signal processing and machine learning algorithms for Electroencephalography (EEG)-based Brain Computer Interface. • Proficiency in programming